{
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  "metadata": {
    "colab": {
      "name": "FinRL_ensemble_stock_trading_ICAIF_2020.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
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    "pycharm": {
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          "collapsed": false
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    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL/blob/master/FinRL_ensemble_stock_trading_ICAIF_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading Using Ensemble Strategy\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks using ensemble strategy in one Jupyter Notebook | Presented at ICAIF 2020\n",
        "\n",
        "* This notebook is the reimplementation of our paper: Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, using FinRL.\n",
        "* Check out medium blog for detailed explanations: https://medium.com/@ai4finance/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPT0ipYE28wL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcf73e63-fece-409e-e887-2d33774cdf94"
      },
      "source": [
        "# ## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.1.0)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.0-cp37-none-any.whl size=38769 sha256=57cad6d53d142ac9069ef5d3f8766b4423ee7cb6bcbd93cefec163ec0021e900\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-93jhahj2/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.59-py2.py3-none-any.whl size=23455 sha256=be79f8c3ff69b2e818062d9e7ca122a7c2177fd210d89411a6ea663e654ec24c\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/2a/0f/4b5a86e1d52e451757eb6bc17fd899629f0925c777741b6d04\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp37-none-any.whl size=75776 sha256=8b44b4da1bf5809c4ab6c0db6f3acc819190b90ac8ceeb96708b44b221598578\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-93jhahj2/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp37-none-any.whl size=39780 sha256=ead98f0c3e98e34ee4d346c0424bcd9eb8bfd614e8c45093c68c9ccbe2dac5fd\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.3.0 int-date-0.1.8 lxml-4.6.3 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-1.0 stockstats-0.3.2 yfinance-0.1.59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeMK7Uentj1V"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_stocktrading import StockTradingEnv\n",
        "from finrl.model.models import DRLAgent,DRLEnsembleAgent\n",
        "from finrl.trade.backtest import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "fe5cd33c-9434-4372-b836-622a50f0d3d7"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2000-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "a42433a2-c7c9-4c06-b53f-28723b4957af"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "b9293979-3d5b-48f6-9b99-6facf5bc5f1e"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-06-20',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (93960, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiRuFOTOtj1Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "015897f3-d0d9-49d6-a28d-d2888e3c7a1f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.698216</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.830360</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.592946</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.787006  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.698216   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.830360    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.592946   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DSw4ZEzVtj1Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "517b31f3-e7b4-419b-9322-517babcd5546"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>93955</th>\n",
              "      <td>2021-06-11</td>\n",
              "      <td>234.389999</td>\n",
              "      <td>235.440002</td>\n",
              "      <td>233.710007</td>\n",
              "      <td>234.960007</td>\n",
              "      <td>5376500</td>\n",
              "      <td>V</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93956</th>\n",
              "      <td>2021-06-11</td>\n",
              "      <td>57.490002</td>\n",
              "      <td>57.549999</td>\n",
              "      <td>57.009998</td>\n",
              "      <td>57.330002</td>\n",
              "      <td>12924100</td>\n",
              "      <td>VZ</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93957</th>\n",
              "      <td>2021-06-11</td>\n",
              "      <td>55.580002</td>\n",
              "      <td>55.820000</td>\n",
              "      <td>54.810001</td>\n",
              "      <td>55.310001</td>\n",
              "      <td>3942600</td>\n",
              "      <td>WBA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93958</th>\n",
              "      <td>2021-06-11</td>\n",
              "      <td>140.229996</td>\n",
              "      <td>140.850006</td>\n",
              "      <td>139.860001</td>\n",
              "      <td>140.750000</td>\n",
              "      <td>8408800</td>\n",
              "      <td>WMT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93959</th>\n",
              "      <td>2021-06-11</td>\n",
              "      <td>63.009998</td>\n",
              "      <td>63.189999</td>\n",
              "      <td>62.139999</td>\n",
              "      <td>62.169998</td>\n",
              "      <td>17618900</td>\n",
              "      <td>XOM</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date        open        high  ...    volume  tic  day\n",
              "93955  2021-06-11  234.389999  235.440002  ...   5376500    V    4\n",
              "93956  2021-06-11   57.490002   57.549999  ...  12924100   VZ    4\n",
              "93957  2021-06-11   55.580002   55.820000  ...   3942600  WBA    4\n",
              "93958  2021-06-11  140.229996  140.850006  ...   8408800  WMT    4\n",
              "93959  2021-06-11   63.009998   63.189999  ...  17618900  XOM    4\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "45525740-8ce0-4031-c2c5-bfd6a7a1cd7a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(93960, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "26d3122c-f143-4671-8891-4ac5ce27d2e8"
      },
      "source": [
        "df.sort_values(['date','tic']).head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.698216</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.830360</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.592946</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.787006  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.698216   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.830360    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.592946   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "jgXfBcjxtj1a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a887a88-8cb0-4d02-c518-374278251850"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.TECHNICAL_INDICATORS_LIST,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_3v-ycktj1b"
      },
      "source": [
        "list_ticker = processed[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "processed_full = processed_full.fillna(0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "grvhGJJII3Xn",
        "outputId": "eded8f63-ac44-4a51-f2bb-4a54a07cbd11"
      },
      "source": [
        "processed_full.sample(5)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>111230</th>\n",
              "      <td>2019-02-26</td>\n",
              "      <td>PFE</td>\n",
              "      <td>40.759014</td>\n",
              "      <td>41.015179</td>\n",
              "      <td>40.607212</td>\n",
              "      <td>37.361835</td>\n",
              "      <td>16548959.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.185043</td>\n",
              "      <td>37.742798</td>\n",
              "      <td>35.504204</td>\n",
              "      <td>52.690392</td>\n",
              "      <td>125.584020</td>\n",
              "      <td>9.121851</td>\n",
              "      <td>36.404776</td>\n",
              "      <td>36.923174</td>\n",
              "      <td>11.697597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118805</th>\n",
              "      <td>2019-11-06</td>\n",
              "      <td>CVX</td>\n",
              "      <td>121.940002</td>\n",
              "      <td>122.139999</td>\n",
              "      <td>119.800003</td>\n",
              "      <td>109.457344</td>\n",
              "      <td>6104700.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.558695</td>\n",
              "      <td>110.849511</td>\n",
              "      <td>103.299800</td>\n",
              "      <td>52.589875</td>\n",
              "      <td>128.847934</td>\n",
              "      <td>17.663018</td>\n",
              "      <td>106.401236</td>\n",
              "      <td>107.718740</td>\n",
              "      <td>20.754927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115317</th>\n",
              "      <td>2019-07-12</td>\n",
              "      <td>WBA</td>\n",
              "      <td>56.250000</td>\n",
              "      <td>56.259998</td>\n",
              "      <td>55.560001</td>\n",
              "      <td>51.566864</td>\n",
              "      <td>3826100.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.798197</td>\n",
              "      <td>52.501365</td>\n",
              "      <td>47.480436</td>\n",
              "      <td>54.312825</td>\n",
              "      <td>121.255626</td>\n",
              "      <td>49.142431</td>\n",
              "      <td>49.198833</td>\n",
              "      <td>48.955927</td>\n",
              "      <td>40.511561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64212</th>\n",
              "      <td>2014-11-12</td>\n",
              "      <td>JNJ</td>\n",
              "      <td>108.669998</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>108.389999</td>\n",
              "      <td>90.559868</td>\n",
              "      <td>4895900.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.382124</td>\n",
              "      <td>94.081265</td>\n",
              "      <td>80.780469</td>\n",
              "      <td>59.015357</td>\n",
              "      <td>102.931019</td>\n",
              "      <td>20.282643</td>\n",
              "      <td>86.566629</td>\n",
              "      <td>87.012509</td>\n",
              "      <td>16.539743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108283</th>\n",
              "      <td>2018-11-20</td>\n",
              "      <td>JPM</td>\n",
              "      <td>109.779999</td>\n",
              "      <td>110.489998</td>\n",
              "      <td>107.980003</td>\n",
              "      <td>100.165672</td>\n",
              "      <td>18936200.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.014552</td>\n",
              "      <td>104.964195</td>\n",
              "      <td>95.325580</td>\n",
              "      <td>47.423803</td>\n",
              "      <td>22.364875</td>\n",
              "      <td>20.839166</td>\n",
              "      <td>99.980946</td>\n",
              "      <td>102.748073</td>\n",
              "      <td>31.748944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              date  tic        open  ...  close_30_sma  close_60_sma  turbulence\n",
              "111230  2019-02-26  PFE   40.759014  ...     36.404776     36.923174   11.697597\n",
              "118805  2019-11-06  CVX  121.940002  ...    106.401236    107.718740   20.754927\n",
              "115317  2019-07-12  WBA   56.250000  ...     49.198833     48.955927   40.511561\n",
              "64212   2014-11-12  JNJ  108.669998  ...     86.566629     87.012509   16.539743\n",
              "108283  2018-11-20  JPM  109.779999  ...     99.980946    102.748073   31.748944\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYN573SOHhxG",
        "outputId": "80acc632-dbc2-4de7-b9cd-0685b130fe42"
      },
      "source": [
        "config.TECHNICAL_INDICATORS_LIST"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['macd',\n",
              " 'boll_ub',\n",
              " 'boll_lb',\n",
              " 'rsi_30',\n",
              " 'cci_30',\n",
              " 'dx_30',\n",
              " 'close_30_sma',\n",
              " 'close_60_sma']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "85af7d0d-3cf3-4473-a62d-e9712c92fe44"
      },
      "source": [
        "stock_dimension = len(processed_full.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(config.TECHNICAL_INDICATORS_LIST)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 301\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"buy_cost_pct\": 0.001, \n",
        "    \"sell_cost_pct\": 0.001, \n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4,\n",
        "    \"print_verbosity\":5\n",
        "    \n",
        "}"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms.\n",
        "\n",
        "* In this notebook, we are training and validating 3 agents (A2C, PPO, DDPG) using Rolling-window Ensemble Method ([reference code](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020/blob/80415db8fa7b2179df6bd7e81ce4fe8dbf913806/model/models.py#L92))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-gthCxMtj1d"
      },
      "source": [
        "rebalance_window = 63 # rebalance_window is the number of days to retrain the model\n",
        "validation_window = 63 # validation_window is the number of days to do validation and trading (e.g. if validation_window=63, then both validation and trading period will be 63 days)\n",
        "train_start = '2009-01-01'\n",
        "train_end = '2019-01-01'\n",
        "val_test_start = '2019-01-01'\n",
        "val_test_end = '2020-07-20'\n",
        "\n",
        "ensemble_agent = DRLEnsembleAgent(df=processed_full,\n",
        "                 train_period=(train_start,train_end),\n",
        "                 val_test_period=(val_test_start,val_test_end),\n",
        "                 rebalance_window=rebalance_window, \n",
        "                 validation_window=validation_window, \n",
        "                 **env_kwargs)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "KsfEHa_Etj1d"
      },
      "source": [
        "A2C_model_kwargs = {\n",
        "                    'n_steps': 5,\n",
        "                    'ent_coef': 0.01,\n",
        "                    'learning_rate': 0.0005\n",
        "                    }\n",
        "\n",
        "PPO_model_kwargs = {\n",
        "                    \"ent_coef\":0.01,\n",
        "                    \"n_steps\": 2048,\n",
        "                    \"learning_rate\": 0.00025,\n",
        "                    \"batch_size\": 128\n",
        "                    }\n",
        "\n",
        "DDPG_model_kwargs = {\n",
        "                      \"action_noise\":\"ornstein_uhlenbeck\",\n",
        "                      \"buffer_size\": 50_000,\n",
        "                      \"learning_rate\": 0.000005,\n",
        "                      \"batch_size\": 128\n",
        "                    }\n",
        "\n",
        "timesteps_dict = {'a2c' : 30_000, \n",
        "                 'ppo' : 100_000, \n",
        "                 'ddpg' : 10_000\n",
        "                 }"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "_1lyCECstj1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "940e556d-8fbd-4112-c463-b155f6a0f581"
      },
      "source": [
        "df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,\n",
        "                                                 PPO_model_kwargs,\n",
        "                                                 DDPG_model_kwargs,\n",
        "                                                 timesteps_dict)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Sharpe: 0.951\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.92e+06 |\n",
            "|    total_cost         | 6.72e+04 |\n",
            "|    total_reward       | 3.92e+06 |\n",
            "|    total_reward_pct   | 392      |\n",
            "|    total_trades       | 52253    |\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 140      |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.314   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | -201     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 30       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 144      |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 1.65e-05 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | -168     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 18.8     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 3000      |\n",
            "|    time_elapsed       | 149       |\n",
            "|    total_timesteps    | 15000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 2999      |\n",
            "|    policy_loss        | 100       |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 9.23      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 154      |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | -27.6    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.64     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 159      |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.0188  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | -343     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 71.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 5.31e+06 |\n",
            "|    total_cost         | 6.82e+04 |\n",
            "|    total_reward       | 4.31e+06 |\n",
            "|    total_reward_pct   | 431      |\n",
            "|    total_trades       | 51027    |\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 164      |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0649   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | -295     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 50       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 169      |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | -28.3    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.81     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 3500      |\n",
            "|    time_elapsed       | 174       |\n",
            "|    total_timesteps    | 17500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 3499      |\n",
            "|    policy_loss        | -134      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 10.5      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 179      |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | -359     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 81.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 184      |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | 81.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.12     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.73e+06 |\n",
            "|    total_cost         | 5.28e+04 |\n",
            "|    total_reward       | 2.73e+06 |\n",
            "|    total_reward_pct   | 273      |\n",
            "|    total_trades       | 51163    |\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3800     |\n",
            "|    time_elapsed       | 189      |\n",
            "|    total_timesteps    | 19000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3799     |\n",
            "|    policy_loss        | -56.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.97     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 194      |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.105    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | 88.2     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.99     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 199      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | 24       |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.23     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 4100      |\n",
            "|    time_elapsed       | 204       |\n",
            "|    total_timesteps    | 20500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -0.000166 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 4099      |\n",
            "|    policy_loss        | 89.2      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 8.61      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 209      |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | -29.9    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.76     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 4300      |\n",
            "|    time_elapsed       | 214       |\n",
            "|    total_timesteps    | 21500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 4299      |\n",
            "|    policy_loss        | -72.6     |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 5.3       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.14e+06 |\n",
            "|    total_cost         | 4.86e+04 |\n",
            "|    total_reward       | 3.14e+06 |\n",
            "|    total_reward_pct   | 314      |\n",
            "|    total_trades       | 50321    |\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 219      |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | 55.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.99     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 224      |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | -0.76    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.0314   |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 229      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 60.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.84     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 234      |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4699     |\n",
            "|    policy_loss        | -133     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 11.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 239      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | 124      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 13       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.81e+06 |\n",
            "|    total_cost         | 2.55e+04 |\n",
            "|    total_reward       | 2.81e+06 |\n",
            "|    total_reward_pct   | 281      |\n",
            "|    total_trades       | 44851    |\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 243      |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | -7.23    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.375    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 248      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.747   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | -155     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 15.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 253      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | -90.6    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.06     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 258      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | -199     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 26.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 263      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 32.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.01     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 5400      |\n",
            "|    time_elapsed       | 268       |\n",
            "|    total_timesteps    | 27000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 5399      |\n",
            "|    policy_loss        | 66        |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 5.7       |\n",
            "-------------------------------------\n",
            "day: 2704, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3561300.10\n",
            "total_reward: 2561300.10\n",
            "total_cost: 40016.11\n",
            "total_trades: 45044\n",
            "Sharpe: 0.882\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.56e+06 |\n",
            "|    total_cost         | 4e+04    |\n",
            "|    total_reward       | 2.56e+06 |\n",
            "|    total_reward_pct   | 256      |\n",
            "|    total_trades       | 45044    |\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 273      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.142    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 45       |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.49     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 278      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 26.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.885    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 283      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 15.7     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.02     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 288      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 80.1     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.96     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 293      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | -137     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 9.1      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.37e+06 |\n",
            "|    total_cost         | 3.86e+04 |\n",
            "|    total_reward       | 2.37e+06 |\n",
            "|    total_reward_pct   | 237      |\n",
            "|    total_trades       | 46456    |\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 298      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | -11.6    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.68     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2019-10-02 to  2020-01-02\n",
            "A2C Sharpe Ratio:  0.3779803731341328\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_315_1\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 108  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 18   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.06e+06    |\n",
            "|    total_cost           | 3.08e+05    |\n",
            "|    total_reward         | 2.06e+06    |\n",
            "|    total_reward_pct     | 206         |\n",
            "|    total_trades         | 78319       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 105         |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 38          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.009495519 |\n",
            "|    clip_fraction        | 0.242       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.00217    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.57        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0266     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 11.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.39e+06    |\n",
            "|    total_cost           | 3.05e+05    |\n",
            "|    total_reward         | 2.39e+06    |\n",
            "|    total_reward_pct     | 239         |\n",
            "|    total_trades         | 77951       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 105         |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 58          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.021563837 |\n",
            "|    clip_fraction        | 0.171       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.0104     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.48        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0234     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 16.7        |\n",
            "-----------------------------------------\n",
            "day: 2704, episode: 15\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2761390.07\n",
            "total_reward: 1761390.07\n",
            "total_cost: 307150.73\n",
            "total_trades: 77960\n",
            "Sharpe: 0.711\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.76e+06    |\n",
            "|    total_cost           | 3.07e+05    |\n",
            "|    total_reward         | 1.76e+06    |\n",
            "|    total_reward_pct     | 176         |\n",
            "|    total_trades         | 77960       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 105         |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 77          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020662857 |\n",
            "|    clip_fraction        | 0.225       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | 0.00629     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 11.6        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0217     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 23          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 97          |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.012240499 |\n",
            "|    clip_fraction        | 0.161       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | -0.0299     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 12.4        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0184     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 24.9        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.86e+06    |\n",
            "|    total_cost           | 3.01e+05    |\n",
            "|    total_reward         | 1.86e+06    |\n",
            "|    total_reward_pct     | 186         |\n",
            "|    total_trades         | 77346       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 6           |\n",
            "|    time_elapsed         | 117         |\n",
            "|    total_timesteps      | 12288       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.009859698 |\n",
            "|    clip_fraction        | 0.204       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | -0.00533    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.3         |\n",
            "|    n_updates            | 50          |\n",
            "|    policy_gradient_loss | -0.0225     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 10.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.01e+06    |\n",
            "|    total_cost           | 2.9e+05     |\n",
            "|    total_reward         | 2.01e+06    |\n",
            "|    total_reward_pct     | 201         |\n",
            "|    total_trades         | 76509       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 7           |\n",
            "|    time_elapsed         | 136         |\n",
            "|    total_timesteps      | 14336       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017503198 |\n",
            "|    clip_fraction        | 0.205       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | -0.0157     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.07        |\n",
            "|    n_updates            | 60          |\n",
            "|    policy_gradient_loss | -0.0185     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 16.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.32e+06    |\n",
            "|    total_cost           | 3.05e+05    |\n",
            "|    total_reward         | 2.32e+06    |\n",
            "|    total_reward_pct     | 232         |\n",
            "|    total_trades         | 77366       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 8           |\n",
            "|    time_elapsed         | 156         |\n",
            "|    total_timesteps      | 16384       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.022058299 |\n",
            "|    clip_fraction        | 0.281       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | -0.00155    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 10.8        |\n",
            "|    n_updates            | 70          |\n",
            "|    policy_gradient_loss | -0.0228     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 20.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 9           |\n",
            "|    time_elapsed         | 176         |\n",
            "|    total_timesteps      | 18432       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017470887 |\n",
            "|    clip_fraction        | 0.224       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43         |\n",
            "|    explained_variance   | -0.0124     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 9.78        |\n",
            "|    n_updates            | 80          |\n",
            "|    policy_gradient_loss | -0.0178     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 18.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.63e+06    |\n",
            "|    total_cost           | 2.87e+05    |\n",
            "|    total_reward         | 2.63e+06    |\n",
            "|    total_reward_pct     | 263         |\n",
            "|    total_trades         | 76266       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 10          |\n",
            "|    time_elapsed         | 196         |\n",
            "|    total_timesteps      | 20480       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025269328 |\n",
            "|    clip_fraction        | 0.229       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43         |\n",
            "|    explained_variance   | 0.0146      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.11        |\n",
            "|    n_updates            | 90          |\n",
            "|    policy_gradient_loss | -0.0195     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 13.8        |\n",
            "-----------------------------------------\n",
            "day: 2704, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3312193.63\n",
            "total_reward: 2312193.63\n",
            "total_cost: 291731.52\n",
            "total_trades: 76371\n",
            "Sharpe: 0.900\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.31e+06    |\n",
            "|    total_cost           | 2.92e+05    |\n",
            "|    total_reward         | 2.31e+06    |\n",
            "|    total_reward_pct     | 231         |\n",
            "|    total_trades         | 76371       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 11          |\n",
            "|    time_elapsed         | 215         |\n",
            "|    total_timesteps      | 22528       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024006503 |\n",
            "|    clip_fraction        | 0.22        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | 0.0192      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.99        |\n",
            "|    n_updates            | 100         |\n",
            "|    policy_gradient_loss | -0.0205     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 19.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.59e+06    |\n",
            "|    total_cost           | 2.92e+05    |\n",
            "|    total_reward         | 1.59e+06    |\n",
            "|    total_reward_pct     | 159         |\n",
            "|    total_trades         | 76416       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 12          |\n",
            "|    time_elapsed         | 235         |\n",
            "|    total_timesteps      | 24576       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.029459687 |\n",
            "|    clip_fraction        | 0.248       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | 0.075       |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.8         |\n",
            "|    n_updates            | 110         |\n",
            "|    policy_gradient_loss | -0.019      |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 20.9        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 13          |\n",
            "|    time_elapsed         | 255         |\n",
            "|    total_timesteps      | 26624       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.008699149 |\n",
            "|    clip_fraction        | 0.2         |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.2       |\n",
            "|    explained_variance   | -0.0224     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.76        |\n",
            "|    n_updates            | 120         |\n",
            "|    policy_gradient_loss | -0.0203     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 14          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.41e+06    |\n",
            "|    total_cost           | 2.7e+05     |\n",
            "|    total_reward         | 1.41e+06    |\n",
            "|    total_reward_pct     | 141         |\n",
            "|    total_trades         | 74655       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 14          |\n",
            "|    time_elapsed         | 275         |\n",
            "|    total_timesteps      | 28672       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015886707 |\n",
            "|    clip_fraction        | 0.239       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | -0.0545     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.34        |\n",
            "|    n_updates            | 130         |\n",
            "|    policy_gradient_loss | -0.025      |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 11.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.45e+06    |\n",
            "|    total_cost           | 2.77e+05    |\n",
            "|    total_reward         | 1.45e+06    |\n",
            "|    total_reward_pct     | 145         |\n",
            "|    total_trades         | 75145       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 15          |\n",
            "|    time_elapsed         | 294         |\n",
            "|    total_timesteps      | 30720       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017444627 |\n",
            "|    clip_fraction        | 0.184       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | -0.0136     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.82        |\n",
            "|    n_updates            | 140         |\n",
            "|    policy_gradient_loss | -0.0138     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 14.1        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.82e+06    |\n",
            "|    total_cost           | 2.83e+05    |\n",
            "|    total_reward         | 1.82e+06    |\n",
            "|    total_reward_pct     | 182         |\n",
            "|    total_trades         | 75686       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 16          |\n",
            "|    time_elapsed         | 314         |\n",
            "|    total_timesteps      | 32768       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019446295 |\n",
            "|    clip_fraction        | 0.179       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | 0.0146      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.91        |\n",
            "|    n_updates            | 150         |\n",
            "|    policy_gradient_loss | -0.0196     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 15.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 17          |\n",
            "|    time_elapsed         | 334         |\n",
            "|    total_timesteps      | 34816       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.027145693 |\n",
            "|    clip_fraction        | 0.263       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | 0.0235      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.39        |\n",
            "|    n_updates            | 160         |\n",
            "|    policy_gradient_loss | -0.0129     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 13.8        |\n",
            "-----------------------------------------\n",
            "day: 2704, episode: 25\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2663151.12\n",
            "total_reward: 1663151.12\n",
            "total_cost: 281937.70\n",
            "total_trades: 75427\n",
            "Sharpe: 0.695\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.66e+06    |\n",
            "|    total_cost           | 2.82e+05    |\n",
            "|    total_reward         | 1.66e+06    |\n",
            "|    total_reward_pct     | 166         |\n",
            "|    total_trades         | 75427       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 18          |\n",
            "|    time_elapsed         | 353         |\n",
            "|    total_timesteps      | 36864       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015531536 |\n",
            "|    clip_fraction        | 0.232       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.4       |\n",
            "|    explained_variance   | 0.00724     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.71        |\n",
            "|    n_updates            | 170         |\n",
            "|    policy_gradient_loss | -0.0143     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 12.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.68e+06    |\n",
            "|    total_cost           | 2.89e+05    |\n",
            "|    total_reward         | 2.68e+06    |\n",
            "|    total_reward_pct     | 268         |\n",
            "|    total_trades         | 75700       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 19          |\n",
            "|    time_elapsed         | 373         |\n",
            "|    total_timesteps      | 38912       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017351905 |\n",
            "|    clip_fraction        | 0.174       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.4       |\n",
            "|    explained_variance   | 0.0049      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.98        |\n",
            "|    n_updates            | 180         |\n",
            "|    policy_gradient_loss | -0.0179     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 16.1        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.07e+06    |\n",
            "|    total_cost           | 2.75e+05    |\n",
            "|    total_reward         | 2.07e+06    |\n",
            "|    total_reward_pct     | 207         |\n",
            "|    total_trades         | 74998       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 20          |\n",
            "|    time_elapsed         | 392         |\n",
            "|    total_timesteps      | 40960       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024218865 |\n",
            "|    clip_fraction        | 0.202       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.4       |\n",
            "|    explained_variance   | 0.0219      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 11.2        |\n",
            "|    n_updates            | 190         |\n",
            "|    policy_gradient_loss | -0.018      |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 23.9        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 21          |\n",
            "|    time_elapsed         | 412         |\n",
            "|    total_timesteps      | 43008       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.037437193 |\n",
            "|    clip_fraction        | 0.276       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | 0.0545      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.91        |\n",
            "|    n_updates            | 200         |\n",
            "|    policy_gradient_loss | -0.0176     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 13.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.82e+06    |\n",
            "|    total_cost           | 2.85e+05    |\n",
            "|    total_reward         | 1.82e+06    |\n",
            "|    total_reward_pct     | 182         |\n",
            "|    total_trades         | 75286       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 22          |\n",
            "|    time_elapsed         | 432         |\n",
            "|    total_timesteps      | 45056       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.029939253 |\n",
            "|    clip_fraction        | 0.278       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.6       |\n",
            "|    explained_variance   | -0.0485     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.15        |\n",
            "|    n_updates            | 210         |\n",
            "|    policy_gradient_loss | -0.0151     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 11.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.42e+06    |\n",
            "|    total_cost           | 2.84e+05    |\n",
            "|    total_reward         | 2.42e+06    |\n",
            "|    total_reward_pct     | 242         |\n",
            "|    total_trades         | 75168       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 23          |\n",
            "|    time_elapsed         | 451         |\n",
            "|    total_timesteps      | 47104       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.034141578 |\n",
            "|    clip_fraction        | 0.259       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.6       |\n",
            "|    explained_variance   | -0.017      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 9.13        |\n",
            "|    n_updates            | 220         |\n",
            "|    policy_gradient_loss | -0.0183     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 18.4        |\n",
            "-----------------------------------------\n",
            "day: 2704, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3394706.09\n",
            "total_reward: 2394706.09\n",
            "total_cost: 287029.08\n",
            "total_trades: 75501\n",
            "Sharpe: 0.800\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.39e+06    |\n",
            "|    total_cost           | 2.87e+05    |\n",
            "|    total_reward         | 2.39e+06    |\n",
            "|    total_reward_pct     | 239         |\n",
            "|    total_trades         | 75501       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 24          |\n",
            "|    time_elapsed         | 471         |\n",
            "|    total_timesteps      | 49152       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.010767957 |\n",
            "|    clip_fraction        | 0.206       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.6       |\n",
            "|    explained_variance   | 0.0481      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 12.9        |\n",
            "|    n_updates            | 230         |\n",
            "|    policy_gradient_loss | -0.019      |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 23.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 25          |\n",
            "|    time_elapsed         | 490         |\n",
            "|    total_timesteps      | 51200       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.022116376 |\n",
            "|    clip_fraction        | 0.195       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.7       |\n",
            "|    explained_variance   | 0.0582      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 15.5        |\n",
            "|    n_updates            | 240         |\n",
            "|    policy_gradient_loss | -0.0145     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 20.3        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 2.35e+06   |\n",
            "|    total_cost           | 2.47e+05   |\n",
            "|    total_reward         | 1.35e+06   |\n",
            "|    total_reward_pct     | 135        |\n",
            "|    total_trades         | 72803      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 104        |\n",
            "|    iterations           | 26         |\n",
            "|    time_elapsed         | 510        |\n",
            "|    total_timesteps      | 53248      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01572589 |\n",
            "|    clip_fraction        | 0.216      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.7      |\n",
            "|    explained_variance   | -0.0608    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.07       |\n",
            "|    n_updates            | 250        |\n",
            "|    policy_gradient_loss | -0.0135    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 8.84       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.43e+06    |\n",
            "|    total_cost           | 2.72e+05    |\n",
            "|    total_reward         | 2.43e+06    |\n",
            "|    total_reward_pct     | 243         |\n",
            "|    total_trades         | 74725       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 27          |\n",
            "|    time_elapsed         | 529         |\n",
            "|    total_timesteps      | 55296       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020301964 |\n",
            "|    clip_fraction        | 0.21        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.7       |\n",
            "|    explained_variance   | -0.069      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.4         |\n",
            "|    n_updates            | 260         |\n",
            "|    policy_gradient_loss | -0.0193     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 12.2        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 2.86e+06   |\n",
            "|    total_cost           | 2.4e+05    |\n",
            "|    total_reward         | 1.86e+06   |\n",
            "|    total_reward_pct     | 186        |\n",
            "|    total_trades         | 72903      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 104        |\n",
            "|    iterations           | 28         |\n",
            "|    time_elapsed         | 549        |\n",
            "|    total_timesteps      | 57344      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03523064 |\n",
            "|    clip_fraction        | 0.28       |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.8      |\n",
            "|    explained_variance   | -0.007     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 8.1        |\n",
            "|    n_updates            | 270        |\n",
            "|    policy_gradient_loss | -0.0178    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 16.6       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 29          |\n",
            "|    time_elapsed         | 568         |\n",
            "|    total_timesteps      | 59392       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.036070667 |\n",
            "|    clip_fraction        | 0.274       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.9       |\n",
            "|    explained_variance   | 0.0215      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.01        |\n",
            "|    n_updates            | 280         |\n",
            "|    policy_gradient_loss | -0.0176     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 17.9        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.68e+06    |\n",
            "|    total_cost           | 2.66e+05    |\n",
            "|    total_reward         | 1.68e+06    |\n",
            "|    total_reward_pct     | 168         |\n",
            "|    total_trades         | 73710       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 30          |\n",
            "|    time_elapsed         | 588         |\n",
            "|    total_timesteps      | 61440       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018652152 |\n",
            "|    clip_fraction        | 0.237       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.9       |\n",
            "|    explained_variance   | 0.0141      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 9.05        |\n",
            "|    n_updates            | 290         |\n",
            "|    policy_gradient_loss | -0.0132     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 17.9        |\n",
            "-----------------------------------------\n",
            "day: 2704, episode: 35\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2519787.59\n",
            "total_reward: 1519787.59\n",
            "total_cost: 209963.29\n",
            "total_trades: 69771\n",
            "Sharpe: 0.661\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.52e+06    |\n",
            "|    total_cost           | 2.1e+05     |\n",
            "|    total_reward         | 1.52e+06    |\n",
            "|    total_reward_pct     | 152         |\n",
            "|    total_trades         | 69771       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 31          |\n",
            "|    time_elapsed         | 607         |\n",
            "|    total_timesteps      | 63488       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024782555 |\n",
            "|    clip_fraction        | 0.306       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44         |\n",
            "|    explained_variance   | -0.0105     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.61        |\n",
            "|    n_updates            | 300         |\n",
            "|    policy_gradient_loss | -0.0159     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 14.1        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 3.08e+06   |\n",
            "|    total_cost           | 2.4e+05    |\n",
            "|    total_reward         | 2.08e+06   |\n",
            "|    total_reward_pct     | 208        |\n",
            "|    total_trades         | 71603      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 104        |\n",
            "|    iterations           | 32         |\n",
            "|    time_elapsed         | 627        |\n",
            "|    total_timesteps      | 65536      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03707753 |\n",
            "|    clip_fraction        | 0.366      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -44.1      |\n",
            "|    explained_variance   | -0.0391    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 6.33       |\n",
            "|    n_updates            | 310        |\n",
            "|    policy_gradient_loss | -0.0119    |\n",
            "|    std                  | 1.05       |\n",
            "|    value_loss           | 16.1       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 33          |\n",
            "|    time_elapsed         | 647         |\n",
            "|    total_timesteps      | 67584       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.055292472 |\n",
            "|    clip_fraction        | 0.3         |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.2       |\n",
            "|    explained_variance   | 0.0113      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 13.9        |\n",
            "|    n_updates            | 320         |\n",
            "|    policy_gradient_loss | -0.0103     |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 22.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.12e+06    |\n",
            "|    total_cost           | 1.82e+05    |\n",
            "|    total_reward         | 1.12e+06    |\n",
            "|    total_reward_pct     | 112         |\n",
            "|    total_trades         | 66540       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 34          |\n",
            "|    time_elapsed         | 667         |\n",
            "|    total_timesteps      | 69632       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014696833 |\n",
            "|    clip_fraction        | 0.231       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.2       |\n",
            "|    explained_variance   | 0.0303      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.92        |\n",
            "|    n_updates            | 330         |\n",
            "|    policy_gradient_loss | -0.00427    |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 14.5        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 2.8e+06    |\n",
            "|    total_cost           | 2.11e+05   |\n",
            "|    total_reward         | 1.8e+06    |\n",
            "|    total_reward_pct     | 180        |\n",
            "|    total_trades         | 70149      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 104        |\n",
            "|    iterations           | 35         |\n",
            "|    time_elapsed         | 687        |\n",
            "|    total_timesteps      | 71680      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03446553 |\n",
            "|    clip_fraction        | 0.278      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -44.3      |\n",
            "|    explained_variance   | 0.155      |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.96       |\n",
            "|    n_updates            | 340        |\n",
            "|    policy_gradient_loss | -0.016     |\n",
            "|    std                  | 1.06       |\n",
            "|    value_loss           | 10.4       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.8e+06     |\n",
            "|    total_cost           | 2.04e+05    |\n",
            "|    total_reward         | 1.8e+06     |\n",
            "|    total_reward_pct     | 180         |\n",
            "|    total_trades         | 69465       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 36          |\n",
            "|    time_elapsed         | 707         |\n",
            "|    total_timesteps      | 73728       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017463885 |\n",
            "|    clip_fraction        | 0.244       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.3       |\n",
            "|    explained_variance   | 0.103       |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.18        |\n",
            "|    n_updates            | 350         |\n",
            "|    policy_gradient_loss | -0.0138     |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 14.2        |\n",
            "-----------------------------------------\n",
            "day: 2704, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2369917.12\n",
            "total_reward: 1369917.12\n",
            "total_cost: 225401.46\n",
            "total_trades: 70802\n",
            "Sharpe: 0.598\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.37e+06    |\n",
            "|    total_cost           | 2.25e+05    |\n",
            "|    total_reward         | 1.37e+06    |\n",
            "|    total_reward_pct     | 137         |\n",
            "|    total_trades         | 70802       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 37          |\n",
            "|    time_elapsed         | 727         |\n",
            "|    total_timesteps      | 75776       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.030039068 |\n",
            "|    clip_fraction        | 0.247       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.3       |\n",
            "|    explained_variance   | 0.0816      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.19        |\n",
            "|    n_updates            | 360         |\n",
            "|    policy_gradient_loss | -0.0113     |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 16.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 38          |\n",
            "|    time_elapsed         | 747         |\n",
            "|    total_timesteps      | 77824       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.010455405 |\n",
            "|    clip_fraction        | 0.162       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.4       |\n",
            "|    explained_variance   | 0.0911      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.36        |\n",
            "|    n_updates            | 370         |\n",
            "|    policy_gradient_loss | -0.00975    |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 13.5        |\n",
            "-----------------------------------------\n",
            "------------------------------------------\n",
            "| environment/            |              |\n",
            "|    portfolio_value      | 3.06e+06     |\n",
            "|    total_cost           | 2.3e+05      |\n",
            "|    total_reward         | 2.06e+06     |\n",
            "|    total_reward_pct     | 206          |\n",
            "|    total_trades         | 71674        |\n",
            "| time/                   |              |\n",
            "|    fps                  | 104          |\n",
            "|    iterations           | 39           |\n",
            "|    time_elapsed         | 767          |\n",
            "|    total_timesteps      | 79872        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0145619605 |\n",
            "|    clip_fraction        | 0.228        |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -44.4        |\n",
            "|    explained_variance   | 0.0975       |\n",
            "|    learning_rate        | 0.00025      |\n",
            "|    loss                 | 6.27         |\n",
            "|    n_updates            | 380          |\n",
            "|    policy_gradient_loss | -0.016       |\n",
            "|    std                  | 1.06         |\n",
            "|    value_loss           | 12.3         |\n",
            "------------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.38e+06    |\n",
            "|    total_cost           | 1.94e+05    |\n",
            "|    total_reward         | 1.38e+06    |\n",
            "|    total_reward_pct     | 138         |\n",
            "|    total_trades         | 68552       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 40          |\n",
            "|    time_elapsed         | 786         |\n",
            "|    total_timesteps      | 81920       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.030926455 |\n",
            "|    clip_fraction        | 0.236       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.4       |\n",
            "|    explained_variance   | -0.0246     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.52        |\n",
            "|    n_updates            | 390         |\n",
            "|    policy_gradient_loss | -0.0166     |\n",
            "|    std                  | 1.07        |\n",
            "|    value_loss           | 19.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.86e+06    |\n",
            "|    total_cost           | 2.4e+05     |\n",
            "|    total_reward         | 1.86e+06    |\n",
            "|    total_reward_pct     | 186         |\n",
            "|    total_trades         | 71913       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 41          |\n",
            "|    time_elapsed         | 806         |\n",
            "|    total_timesteps      | 83968       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017606972 |\n",
            "|    clip_fraction        | 0.289       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.5       |\n",
            "|    explained_variance   | -0.00338    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.9         |\n",
            "|    n_updates            | 400         |\n",
            "|    policy_gradient_loss | -0.0176     |\n",
            "|    std                  | 1.07        |\n",
            "|    value_loss           | 13.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 42          |\n",
            "|    time_elapsed         | 826         |\n",
            "|    total_timesteps      | 86016       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.028206393 |\n",
            "|    clip_fraction        | 0.27        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.6       |\n",
            "|    explained_variance   | 0.0515      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 10.2        |\n",
            "|    n_updates            | 410         |\n",
            "|    policy_gradient_loss | -0.0108     |\n",
            "|    std                  | 1.07        |\n",
            "|    value_loss           | 22.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.16e+06    |\n",
            "|    total_cost           | 2.58e+05    |\n",
            "|    total_reward         | 2.16e+06    |\n",
            "|    total_reward_pct     | 216         |\n",
            "|    total_trades         | 72761       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 43          |\n",
            "|    time_elapsed         | 845         |\n",
            "|    total_timesteps      | 88064       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.046707675 |\n",
            "|    clip_fraction        | 0.313       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.6       |\n",
            "|    explained_variance   | -0.00607    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.17        |\n",
            "|    n_updates            | 420         |\n",
            "|    policy_gradient_loss | -0.0109     |\n",
            "|    std                  | 1.07        |\n",
            "|    value_loss           | 17.9        |\n",
            "-----------------------------------------\n",
            "day: 2704, episode: 45\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3456163.99\n",
            "total_reward: 2456163.99\n",
            "total_cost: 266572.69\n",
            "total_trades: 72883\n",
            "Sharpe: 0.760\n",
            "=================================\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 3.46e+06   |\n",
            "|    total_cost           | 2.67e+05   |\n",
            "|    total_reward         | 2.46e+06   |\n",
            "|    total_reward_pct     | 246        |\n",
            "|    total_trades         | 72883      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 104        |\n",
            "|    iterations           | 44         |\n",
            "|    time_elapsed         | 865        |\n",
            "|    total_timesteps      | 90112      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02640852 |\n",
            "|    clip_fraction        | 0.309      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -44.7      |\n",
            "|    explained_variance   | 0.0536     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 12         |\n",
            "|    n_updates            | 430        |\n",
            "|    policy_gradient_loss | -0.0065    |\n",
            "|    std                  | 1.08       |\n",
            "|    value_loss           | 29.2       |\n",
            "----------------------------------------\n",
            "---------------------------------------\n",
            "| environment/            |           |\n",
            "|    portfolio_value      | 3.5e+06   |\n",
            "|    total_cost           | 2.73e+05  |\n",
            "|    total_reward         | 2.5e+06   |\n",
            "|    total_reward_pct     | 250       |\n",
            "|    total_trades         | 73696     |\n",
            "| time/                   |           |\n",
            "|    fps                  | 104       |\n",
            "|    iterations           | 45        |\n",
            "|    time_elapsed         | 884       |\n",
            "|    total_timesteps      | 92160     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0182605 |\n",
            "|    clip_fraction        | 0.248     |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -44.8     |\n",
            "|    explained_variance   | 0.0281    |\n",
            "|    learning_rate        | 0.00025   |\n",
            "|    loss                 | 21        |\n",
            "|    n_updates            | 440       |\n",
            "|    policy_gradient_loss | -0.0103   |\n",
            "|    std                  | 1.08      |\n",
            "|    value_loss           | 44.1      |\n",
            "---------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 46          |\n",
            "|    time_elapsed         | 903         |\n",
            "|    total_timesteps      | 94208       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.035522565 |\n",
            "|    clip_fraction        | 0.293       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.8       |\n",
            "|    explained_variance   | 0.0124      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 24.2        |\n",
            "|    n_updates            | 450         |\n",
            "|    policy_gradient_loss | -0.00464    |\n",
            "|    std                  | 1.08        |\n",
            "|    value_loss           | 36.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.47e+06    |\n",
            "|    total_cost           | 2.6e+05     |\n",
            "|    total_reward         | 2.47e+06    |\n",
            "|    total_reward_pct     | 247         |\n",
            "|    total_trades         | 73233       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 47          |\n",
            "|    time_elapsed         | 923         |\n",
            "|    total_timesteps      | 96256       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.038549464 |\n",
            "|    clip_fraction        | 0.298       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.8       |\n",
            "|    explained_variance   | -0.0176     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.85        |\n",
            "|    n_updates            | 460         |\n",
            "|    policy_gradient_loss | -0.00532    |\n",
            "|    std                  | 1.08        |\n",
            "|    value_loss           | 15.9        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.91e+06    |\n",
            "|    total_cost           | 2.55e+05    |\n",
            "|    total_reward         | 1.91e+06    |\n",
            "|    total_reward_pct     | 191         |\n",
            "|    total_trades         | 72039       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 48          |\n",
            "|    time_elapsed         | 942         |\n",
            "|    total_timesteps      | 98304       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025797598 |\n",
            "|    clip_fraction        | 0.239       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.9       |\n",
            "|    explained_variance   | 0.00616     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 11          |\n",
            "|    n_updates            | 470         |\n",
            "|    policy_gradient_loss | -0.0143     |\n",
            "|    std                  | 1.08        |\n",
            "|    value_loss           | 22.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.72e+06    |\n",
            "|    total_cost           | 2.52e+05    |\n",
            "|    total_reward         | 2.72e+06    |\n",
            "|    total_reward_pct     | 272         |\n",
            "|    total_trades         | 72321       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 49          |\n",
            "|    time_elapsed         | 962         |\n",
            "|    total_timesteps      | 100352      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.029289335 |\n",
            "|    clip_fraction        | 0.186       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.9       |\n",
            "|    explained_variance   | 0.0774      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.58        |\n",
            "|    n_updates            | 480         |\n",
            "|    policy_gradient_loss | -0.0123     |\n",
            "|    std                  | 1.08        |\n",
            "|    value_loss           | 21.9        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2019-10-02 to  2020-01-02\n",
            "PPO Sharpe Ratio:  0.3037062608942089\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_315_1\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 4.27e+06 |\n",
            "|    total_cost       | 1.32e+03 |\n",
            "|    total_reward     | 3.27e+06 |\n",
            "|    total_reward_pct | 327      |\n",
            "|    total_trades     | 35120    |\n",
            "| time/               |          |\n",
            "|    episodes         | 4        |\n",
            "|    fps              | 35       |\n",
            "|    time_elapsed     | 303      |\n",
            "|    total timesteps  | 10820    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 81.3     |\n",
            "|    critic_loss      | 72.2     |\n",
            "|    learning_rate    | 5e-06    |\n",
            "|    n_updates        | 8115     |\n",
            "----------------------------------\n",
            "======DDPG Validation from:  2019-10-02 to  2020-01-02\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-01-02\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/ensemble_315_1\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -2.49    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -21.1    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.438    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 10       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.611    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -74.6    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.27     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 15        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -313      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 53        |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 20       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.155   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 152      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 14.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 25       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.0086   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 297      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 62.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.54e+06 |\n",
            "|    total_cost         | 1.93e+05 |\n",
            "|    total_reward       | 3.54e+06 |\n",
            "|    total_reward_pct   | 354      |\n",
            "|    total_trades       | 67574    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 30       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.157   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 35.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.06     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 35       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.187    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -47.1    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.9      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | 104      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 7.7      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 45       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | -6.33    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.738    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1000      |\n",
            "|    time_elapsed       | 50        |\n",
            "|    total_timesteps    | 5000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 999       |\n",
            "|    policy_loss        | 202       |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 23        |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 55        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | 192       |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 35.5      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 5.63e+06 |\n",
            "|    total_cost         | 1.19e+05 |\n",
            "|    total_reward       | 4.63e+06 |\n",
            "|    total_reward_pct   | 463      |\n",
            "|    total_trades       | 61280    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 61       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.0128   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -3.39    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.407    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 66       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | -1.67    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.374    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 71       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.107    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 202      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 30.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 76       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | -188     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 24       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 81       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.0592  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 7.87     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.37     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 5.08e+06 |\n",
            "|    total_cost         | 1.82e+05 |\n",
            "|    total_reward       | 4.08e+06 |\n",
            "|    total_reward_pct   | 408      |\n",
            "|    total_trades       | 66730    |\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 86       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.0333  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | 0.383    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.86     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 92       |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.72    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 49.4     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 9.13     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.0267   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -92.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.71     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 102      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | -102     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 8.72     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 107      |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 94.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 112      |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.0991   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | 101      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 9.97     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.72e+06 |\n",
            "|    total_cost         | 1.6e+05  |\n",
            "|    total_reward       | 1.72e+06 |\n",
            "|    total_reward_pct   | 172      |\n",
            "|    total_trades       | 64561    |\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 118      |\n",
            "|    total_timesteps    | 11500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.106    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2299     |\n",
            "|    policy_loss        | 49.6     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.16     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 123      |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.517   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | -28      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.848    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 128      |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 4.68e-05 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | 3.29     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.393    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 133      |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | -29      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.975    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 138      |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.515   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | 100      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 9.13     |\n",
            "------------------------------------\n",
            "day: 2767, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3446436.69\n",
            "total_reward: 2446436.69\n",
            "total_cost: 157000.23\n",
            "total_trades: 61151\n",
            "Sharpe: 0.814\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.45e+06 |\n",
            "|    total_cost         | 1.57e+05 |\n",
            "|    total_reward       | 2.45e+06 |\n",
            "|    total_reward_pct   | 245      |\n",
            "|    total_trades       | 61151    |\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 144      |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.0695  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | -34.5    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.902    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 149      |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.017    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | -189     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 51       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 154      |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.00129 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | -69.5    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.81     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 159      |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | -45.9    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 9.22     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 96        |\n",
            "|    iterations         | 3200      |\n",
            "|    time_elapsed       | 164       |\n",
            "|    total_timesteps    | 16000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.1     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 3199      |\n",
            "|    policy_loss        | 3.71      |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 0.486     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 170      |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | 88.3     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 7.96     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.01e+06 |\n",
            "|    total_cost         | 1.59e+05 |\n",
            "|    total_reward       | 2.01e+06 |\n",
            "|    total_reward_pct   | 201      |\n",
            "|    total_trades       | 64795    |\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 175      |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.0408   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | -80.2    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 4.72     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 180      |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | -35.2    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.09     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 185      |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.092    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | 17       |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.472    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 191      |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.0186   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | 258      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 44.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 3800     |\n",
            "|    time_elapsed       | 196      |\n",
            "|    total_timesteps    | 19000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.0242   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3799     |\n",
            "|    policy_loss        | -216     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 33       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.51e+06 |\n",
            "|    total_cost         | 2.37e+05 |\n",
            "|    total_reward       | 2.51e+06 |\n",
            "|    total_reward_pct   | 251      |\n",
            "|    total_trades       | 69559    |\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 201      |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | -0.616   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | -93.7    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 8.39     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 206      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | -0.822   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | 38.4     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.51     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 212      |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.000346 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | -35.7    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.34     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 96        |\n",
            "|    iterations         | 4200      |\n",
            "|    time_elapsed       | 217       |\n",
            "|    total_timesteps    | 21000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43       |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 4199      |\n",
            "|    policy_loss        | 42.8      |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 5.05      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 222      |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.097    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | 121      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 12.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 227      |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.0202   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | -566     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 177      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.28e+06 |\n",
            "|    total_cost         | 1.55e+05 |\n",
            "|    total_reward       | 3.28e+06 |\n",
            "|    total_reward_pct   | 328      |\n",
            "|    total_trades       | 65625    |\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 232      |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | -99.3    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 9.78     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 238      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | -147     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 15       |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 96        |\n",
            "|    iterations         | 4700      |\n",
            "|    time_elapsed       | 243       |\n",
            "|    total_timesteps    | 23500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 4699      |\n",
            "|    policy_loss        | -83.9     |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 5.64      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 248      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.00345  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | -30.9    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.24     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 253      |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | 256      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 35       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.31e+06 |\n",
            "|    total_cost         | 1.66e+05 |\n",
            "|    total_reward       | 2.31e+06 |\n",
            "|    total_reward_pct   | 231      |\n",
            "|    total_trades       | 63168    |\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 258      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.00288 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 127      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 9.68     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 264      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.333    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 140      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 12.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 269      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.102    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 122      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 9.04     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 274      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.0166   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | -19.6    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.08     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 279      |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | -35.4    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.902    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 284      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 105      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 14.7     |\n",
            "------------------------------------\n",
            "day: 2767, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4830520.99\n",
            "total_reward: 3830520.99\n",
            "total_cost: 99864.99\n",
            "total_trades: 57414\n",
            "Sharpe: 0.910\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.83e+06 |\n",
            "|    total_cost         | 9.99e+04 |\n",
            "|    total_reward       | 3.83e+06 |\n",
            "|    total_reward_pct   | 383      |\n",
            "|    total_trades       | 57414    |\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 289      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | -0.0209  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 12.4     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.24     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 295      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | -0.0776  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 8.84     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.26     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 300      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 7.23     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.01     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 305      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 159      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 21.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 310      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | -341     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 97.7     |\n",
            "------------------------------------\n",
            "======Trading from:  2020-01-02 to  2020-04-02\n",
            "============================================\n",
            "nan\n",
            "turbulence_threshold:  332.84948402288677\n",
            "======Model training from:  2009-01-01 to  2020-01-02\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0005}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/a2c/a2c_378_1\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 5        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 15.7     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.311    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 10       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0744   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -17.1    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.498    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 15       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -205     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 25.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 20       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 113      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 7.87     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 25        |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.6     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 207       |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 31        |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.43e+06 |\n",
            "|    total_cost         | 1.39e+05 |\n",
            "|    total_reward       | 2.43e+06 |\n",
            "|    total_reward_pct   | 243      |\n",
            "|    total_trades       | 61712    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 30       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 35       |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.43     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 35       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -255     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 48.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.28     |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -12      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.432    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 45       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | -36.2    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.47     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 51       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | 57.3     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 2.47     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 56       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | 41.2     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.77     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.24e+06 |\n",
            "|    total_cost         | 1.64e+05 |\n",
            "|    total_reward       | 2.24e+06 |\n",
            "|    total_reward_pct   | 224      |\n",
            "|    total_trades       | 63783    |\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 61       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.00492  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -26.5    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 0.512    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 66       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -1.27    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 66.3     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 10.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 71       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0196   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 118      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 8.99     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 76       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | -27.8    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.5      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 81       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | -118     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 17.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.69e+06 |\n",
            "|    total_cost         | 1.67e+05 |\n",
            "|    total_reward       | 3.69e+06 |\n",
            "|    total_reward_pct   | 369      |\n",
            "|    total_trades       | 64416    |\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 87       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0597  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | 45.3     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.48     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 92       |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0943   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 39.8     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.58     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.00217  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -104     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 8.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 102      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | -238     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 31.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 107      |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0.0248   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 88.1     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 9.19     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 112      |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | 284      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 48.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.94e+06 |\n",
            "|    total_cost         | 1.14e+05 |\n",
            "|    total_reward       | 2.94e+06 |\n",
            "|    total_reward_pct   | 294      |\n",
            "|    total_trades       | 62487    |\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 117      |\n",
            "|    total_timesteps    | 11500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.0524  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2299     |\n",
            "|    policy_loss        | 53.1     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.57     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 122      |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.277    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | -72.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.96     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 127      |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | -15.8    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.246    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 132      |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.00989 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | -34.7    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.24     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 137      |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.00743  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | 178      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 20.2     |\n",
            "------------------------------------\n",
            "day: 2767, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3973268.89\n",
            "total_reward: 2973268.89\n",
            "total_cost: 74185.44\n",
            "total_trades: 58320\n",
            "Sharpe: 0.939\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.97e+06 |\n",
            "|    total_cost         | 7.42e+04 |\n",
            "|    total_reward       | 2.97e+06 |\n",
            "|    total_reward_pct   | 297      |\n",
            "|    total_trades       | 58320    |\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 142      |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | -22.1    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.839    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 148      |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.0436   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | -20.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 13.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 153      |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | -43.3    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.15     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 3100      |\n",
            "|    time_elapsed       | 158       |\n",
            "|    total_timesteps    | 15500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.9     |\n",
            "|    explained_variance | -0.000981 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 3099      |\n",
            "|    policy_loss        | 68.5      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 4.35      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 163      |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | 21.4     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.36     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 167      |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | -191     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 33.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.9e+06  |\n",
            "|    total_cost         | 4.55e+04 |\n",
            "|    total_reward       | 3.9e+06  |\n",
            "|    total_reward_pct   | 390      |\n",
            "|    total_trades       | 56765    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 173      |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.00508 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | -92.8    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.65     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 178      |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | -59      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.8      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 183      |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.0123  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | -64.1    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 6.29     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 188      |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.0223  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | 388      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 129      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 193       |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | -456      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 136       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 5.41e+06 |\n",
            "|    total_cost         | 7.55e+04 |\n",
            "|    total_reward       | 4.41e+06 |\n",
            "|    total_reward_pct   | 441      |\n",
            "|    total_trades       | 56925    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 198      |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.0121  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | -145     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 15.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 203      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | 7.11     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.735    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 208      |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.0051   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | -38.3    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.57     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 213      |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.056   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | 40.1     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 10.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 218      |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | -0.121   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | 27.5     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.35     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 223      |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.0595  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | -610     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 215      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 6.59e+06 |\n",
            "|    total_cost         | 9.79e+04 |\n",
            "|    total_reward       | 5.59e+06 |\n",
            "|    total_reward_pct   | 559      |\n",
            "|    total_trades       | 54393    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 228      |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.113   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | -71.8    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 7.68     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 233      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.146    |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | -188     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 21.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 238      |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4699     |\n",
            "|    policy_loss        | -107     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 7.53     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 243      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.0617  |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | 77.8     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 7.8      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 4900      |\n",
            "|    time_elapsed       | 248       |\n",
            "|    total_timesteps    | 24500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.1     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0005    |\n",
            "|    n_updates          | 4899      |\n",
            "|    policy_loss        | 231       |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 46.6      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 5.54e+06 |\n",
            "|    total_cost         | 6.7e+04  |\n",
            "|    total_reward       | 4.54e+06 |\n",
            "|    total_reward_pct   | 454      |\n",
            "|    total_trades       | 55275    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 253      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | -0.119   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 136      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 12       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 258      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 141      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 13.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 263      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 80.7     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 4.78     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 268      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.0766   |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 5.36     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 4.06     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 273      |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | -105     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 6.33     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 278      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | -625     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 235      |\n",
            "------------------------------------\n",
            "day: 2767, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5825615.29\n",
            "total_reward: 4825615.29\n",
            "total_cost: 76837.37\n",
            "total_trades: 61052\n",
            "Sharpe: 0.982\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 5.83e+06 |\n",
            "|    total_cost         | 7.68e+04 |\n",
            "|    total_reward       | 4.83e+06 |\n",
            "|    total_reward_pct   | 483      |\n",
            "|    total_trades       | 61052    |\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 283      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | -21.6    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.399    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 288      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 10.5     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.545    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 294      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 48.7     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.77     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 299      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 203      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 27.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 304      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0005   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | -248     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 46.6     |\n",
            "------------------------------------\n",
            "======A2C Validation from:  2020-01-02 to  2020-04-02\n",
            "A2C Sharpe Ratio:  -0.5093862959047678\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ppo/ppo_378_1\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 108  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 18   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3e+06       |\n",
            "|    total_cost           | 3.21e+05    |\n",
            "|    total_reward         | 2e+06       |\n",
            "|    total_reward_pct     | 200         |\n",
            "|    total_trades         | 79636       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 39          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017370546 |\n",
            "|    clip_fraction        | 0.226       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.0236     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.41        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0309     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 12.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.86e+06    |\n",
            "|    total_cost           | 3.17e+05    |\n",
            "|    total_reward         | 1.86e+06    |\n",
            "|    total_reward_pct     | 186         |\n",
            "|    total_trades         | 79750       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 103         |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 59          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.013988638 |\n",
            "|    clip_fraction        | 0.188       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.00429    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.5         |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0258     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 14.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 103         |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 79          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019524755 |\n",
            "|    clip_fraction        | 0.261       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.0116     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.45        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0239     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 16.9        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 4.8e+06     |\n",
            "|    total_cost           | 3.08e+05    |\n",
            "|    total_reward         | 3.8e+06     |\n",
            "|    total_reward_pct     | 380         |\n",
            "|    total_trades         | 78745       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 99          |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018918531 |\n",
            "|    clip_fraction        | 0.192       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | 0.00966     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 10.6        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.021      |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 31.5        |\n",
            "-----------------------------------------\n",
            "day: 2767, episode: 15\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2385873.76\n",
            "total_reward: 1385873.76\n",
            "total_cost: 295237.17\n",
            "total_trades: 78138\n",
            "Sharpe: 0.585\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.39e+06    |\n",
            "|    total_cost           | 2.95e+05    |\n",
            "|    total_reward         | 1.39e+06    |\n",
            "|    total_reward_pct     | 139         |\n",
            "|    total_trades         | 78138       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 6           |\n",
            "|    time_elapsed         | 119         |\n",
            "|    total_timesteps      | 12288       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014533535 |\n",
            "|    clip_fraction        | 0.21        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | 0.00154     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 12.2        |\n",
            "|    n_updates            | 50          |\n",
            "|    policy_gradient_loss | -0.0278     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 17.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.43e+06    |\n",
            "|    total_cost           | 3.14e+05    |\n",
            "|    total_reward         | 2.43e+06    |\n",
            "|    total_reward_pct     | 243         |\n",
            "|    total_trades         | 78701       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 7           |\n",
            "|    time_elapsed         | 139         |\n",
            "|    total_timesteps      | 14336       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015148232 |\n",
            "|    clip_fraction        | 0.165       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | -0.0977     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.6         |\n",
            "|    n_updates            | 60          |\n",
            "|    policy_gradient_loss | -0.0195     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 14.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 8           |\n",
            "|    time_elapsed         | 160         |\n",
            "|    total_timesteps      | 16384       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014140813 |\n",
            "|    clip_fraction        | 0.173       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | -0.00939    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 19.5        |\n",
            "|    n_updates            | 70          |\n",
            "|    policy_gradient_loss | -0.0176     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 30.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.19e+06    |\n",
            "|    total_cost           | 3.05e+05    |\n",
            "|    total_reward         | 2.19e+06    |\n",
            "|    total_reward_pct     | 219         |\n",
            "|    total_trades         | 78384       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 9           |\n",
            "|    time_elapsed         | 180         |\n",
            "|    total_timesteps      | 18432       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.026928019 |\n",
            "|    clip_fraction        | 0.275       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | 0.00218     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 17.9        |\n",
            "|    n_updates            | 80          |\n",
            "|    policy_gradient_loss | -0.0192     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 28.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.41e+06    |\n",
            "|    total_cost           | 2.96e+05    |\n",
            "|    total_reward         | 1.41e+06    |\n",
            "|    total_reward_pct     | 141         |\n",
            "|    total_trades         | 77986       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 10          |\n",
            "|    time_elapsed         | 200         |\n",
            "|    total_timesteps      | 20480       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.023487184 |\n",
            "|    clip_fraction        | 0.247       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43         |\n",
            "|    explained_variance   | -0.0369     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.99        |\n",
            "|    n_updates            | 90          |\n",
            "|    policy_gradient_loss | -0.0234     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 13.8        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 3.9e+06    |\n",
            "|    total_cost           | 3.09e+05   |\n",
            "|    total_reward         | 2.9e+06    |\n",
            "|    total_reward_pct     | 290        |\n",
            "|    total_trades         | 78541      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 102        |\n",
            "|    iterations           | 11         |\n",
            "|    time_elapsed         | 220        |\n",
            "|    total_timesteps      | 22528      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03453187 |\n",
            "|    clip_fraction        | 0.291      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43        |\n",
            "|    explained_variance   | 0.00103    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 7.09       |\n",
            "|    n_updates            | 100        |\n",
            "|    policy_gradient_loss | -0.0259    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 15.4       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 12          |\n",
            "|    time_elapsed         | 240         |\n",
            "|    total_timesteps      | 24576       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.013325006 |\n",
            "|    clip_fraction        | 0.186       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | 0.00658     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 16.9        |\n",
            "|    n_updates            | 110         |\n",
            "|    policy_gradient_loss | -0.0175     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 35          |\n",
            "-----------------------------------------\n",
            "day: 2767, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3120961.93\n",
            "total_reward: 2120961.93\n",
            "total_cost: 302863.85\n",
            "total_trades: 78280\n",
            "Sharpe: 0.691\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.12e+06    |\n",
            "|    total_cost           | 3.03e+05    |\n",
            "|    total_reward         | 2.12e+06    |\n",
            "|    total_reward_pct     | 212         |\n",
            "|    total_trades         | 78280       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 13          |\n",
            "|    time_elapsed         | 261         |\n",
            "|    total_timesteps      | 26624       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019859375 |\n",
            "|    clip_fraction        | 0.219       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | -0.0404     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 13.1        |\n",
            "|    n_updates            | 120         |\n",
            "|    policy_gradient_loss | -0.0201     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 22.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.14e+06    |\n",
            "|    total_cost           | 3.05e+05    |\n",
            "|    total_reward         | 2.14e+06    |\n",
            "|    total_reward_pct     | 214         |\n",
            "|    total_trades         | 78254       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 14          |\n",
            "|    time_elapsed         | 281         |\n",
            "|    total_timesteps      | 28672       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.029500125 |\n",
            "|    clip_fraction        | 0.269       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | 0.00424     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.38        |\n",
            "|    n_updates            | 130         |\n",
            "|    policy_gradient_loss | -0.0234     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 17.2        |\n",
            "-----------------------------------------\n",
            "--------------------------------------\n",
            "| environment/            |          |\n",
            "|    portfolio_value      | 2.87e+06 |\n",
            "|    total_cost           | 3.01e+05 |\n",
            "|    total_reward         | 1.87e+06 |\n",
            "|    total_reward_pct     | 187      |\n",
            "|    total_trades         | 77722    |\n",
            "| time/                   |          |\n",
            "|    fps                  | 101      |\n",
            "|    iterations           | 15       |\n",
            "|    time_elapsed         | 301      |\n",
            "|    total_timesteps      | 30720    |\n",
            "| train/                  |          |\n",
            "|    approx_kl            | 0.021112 |\n",
            "|    clip_fraction        | 0.299    |\n",
            "|    clip_range           | 0.2      |\n",
            "|    entropy_loss         | -43.1    |\n",
            "|    explained_variance   | -0.00895 |\n",
            "|    learning_rate        | 0.00025  |\n",
            "|    loss                 | 6.53     |\n",
            "|    n_updates            | 140      |\n",
            "|    policy_gradient_loss | -0.0203  |\n",
            "|    std                  | 1.02     |\n",
            "|    value_loss           | 18.1     |\n",
            "--------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 16          |\n",
            "|    time_elapsed         | 321         |\n",
            "|    total_timesteps      | 32768       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.027718194 |\n",
            "|    clip_fraction        | 0.232       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.1       |\n",
            "|    explained_variance   | -0.0171     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 12.7        |\n",
            "|    n_updates            | 150         |\n",
            "|    policy_gradient_loss | -0.0146     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 28.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.26e+06    |\n",
            "|    total_cost           | 2.93e+05    |\n",
            "|    total_reward         | 2.26e+06    |\n",
            "|    total_reward_pct     | 226         |\n",
            "|    total_trades         | 77325       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 17          |\n",
            "|    time_elapsed         | 341         |\n",
            "|    total_timesteps      | 34816       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.030979449 |\n",
            "|    clip_fraction        | 0.235       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.2       |\n",
            "|    explained_variance   | -0.0375     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.7         |\n",
            "|    n_updates            | 160         |\n",
            "|    policy_gradient_loss | -0.0172     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 17.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.88e+06    |\n",
            "|    total_cost           | 2.7e+05     |\n",
            "|    total_reward         | 1.88e+06    |\n",
            "|    total_reward_pct     | 188         |\n",
            "|    total_trades         | 75666       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 18          |\n",
            "|    time_elapsed         | 361         |\n",
            "|    total_timesteps      | 36864       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018998317 |\n",
            "|    clip_fraction        | 0.236       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.2       |\n",
            "|    explained_variance   | -0.0128     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 12.9        |\n",
            "|    n_updates            | 170         |\n",
            "|    policy_gradient_loss | -0.0144     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 21.8        |\n",
            "-----------------------------------------\n",
            "day: 2767, episode: 25\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3235356.08\n",
            "total_reward: 2235356.08\n",
            "total_cost: 296480.08\n",
            "total_trades: 77285\n",
            "Sharpe: 0.773\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.24e+06    |\n",
            "|    total_cost           | 2.96e+05    |\n",
            "|    total_reward         | 2.24e+06    |\n",
            "|    total_reward_pct     | 224         |\n",
            "|    total_trades         | 77285       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 19          |\n",
            "|    time_elapsed         | 380         |\n",
            "|    total_timesteps      | 38912       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.038185775 |\n",
            "|    clip_fraction        | 0.24        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | 0.0132      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 17          |\n",
            "|    n_updates            | 180         |\n",
            "|    policy_gradient_loss | -0.0179     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 36.3        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 102        |\n",
            "|    iterations           | 20         |\n",
            "|    time_elapsed         | 401        |\n",
            "|    total_timesteps      | 40960      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02639893 |\n",
            "|    clip_fraction        | 0.237      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.3      |\n",
            "|    explained_variance   | -0.0122    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 8.44       |\n",
            "|    n_updates            | 190        |\n",
            "|    policy_gradient_loss | -0.0168    |\n",
            "|    std                  | 1.03       |\n",
            "|    value_loss           | 21.1       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 4.03e+06    |\n",
            "|    total_cost           | 2.93e+05    |\n",
            "|    total_reward         | 3.03e+06    |\n",
            "|    total_reward_pct     | 303         |\n",
            "|    total_trades         | 77348       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 21          |\n",
            "|    time_elapsed         | 421         |\n",
            "|    total_timesteps      | 43008       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.033775594 |\n",
            "|    clip_fraction        | 0.255       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | 0.016       |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.56        |\n",
            "|    n_updates            | 200         |\n",
            "|    policy_gradient_loss | -0.0225     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 13.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.64e+06    |\n",
            "|    total_cost           | 2.83e+05    |\n",
            "|    total_reward         | 2.64e+06    |\n",
            "|    total_reward_pct     | 264         |\n",
            "|    total_trades         | 76439       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 22          |\n",
            "|    time_elapsed         | 441         |\n",
            "|    total_timesteps      | 45056       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024415996 |\n",
            "|    clip_fraction        | 0.201       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.4       |\n",
            "|    explained_variance   | 0.0632      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.29        |\n",
            "|    n_updates            | 210         |\n",
            "|    policy_gradient_loss | -0.016      |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 25.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.64e+06    |\n",
            "|    total_cost           | 2.81e+05    |\n",
            "|    total_reward         | 1.64e+06    |\n",
            "|    total_reward_pct     | 164         |\n",
            "|    total_trades         | 76298       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 23          |\n",
            "|    time_elapsed         | 461         |\n",
            "|    total_timesteps      | 47104       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.031352326 |\n",
            "|    clip_fraction        | 0.298       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | 0.00488     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 9.44        |\n",
            "|    n_updates            | 220         |\n",
            "|    policy_gradient_loss | -0.017      |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 21.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 24          |\n",
            "|    time_elapsed         | 481         |\n",
            "|    total_timesteps      | 49152       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.023647495 |\n",
            "|    clip_fraction        | 0.2         |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | 0.02        |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 14.7        |\n",
            "|    n_updates            | 230         |\n",
            "|    policy_gradient_loss | -0.0167     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 25.6        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 4.13e+06   |\n",
            "|    total_cost           | 3.01e+05   |\n",
            "|    total_reward         | 3.13e+06   |\n",
            "|    total_reward_pct     | 313        |\n",
            "|    total_trades         | 77486      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 102        |\n",
            "|    iterations           | 25         |\n",
            "|    time_elapsed         | 501        |\n",
            "|    total_timesteps      | 51200      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02821264 |\n",
            "|    clip_fraction        | 0.229      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.6      |\n",
            "|    explained_variance   | -0.00137   |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 7.29       |\n",
            "|    n_updates            | 240        |\n",
            "|    policy_gradient_loss | -0.0164    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 14.7       |\n",
            "----------------------------------------\n",
            "day: 2767, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2867012.63\n",
            "total_reward: 1867012.63\n",
            "total_cost: 287974.32\n",
            "total_trades: 76619\n",
            "Sharpe: 0.715\n",
            "=================================\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 2.87e+06   |\n",
            "|    total_cost           | 2.88e+05   |\n",
            "|    total_reward         | 1.87e+06   |\n",
            "|    total_reward_pct     | 187        |\n",
            "|    total_trades         | 76619      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 102        |\n",
            "|    iterations           | 26         |\n",
            "|    time_elapsed         | 521        |\n",
            "|    total_timesteps      | 53248      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02154628 |\n",
            "|    clip_fraction        | 0.145      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.6      |\n",
            "|    explained_variance   | 0.0662     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 16.6       |\n",
            "|    n_updates            | 250        |\n",
            "|    policy_gradient_loss | -0.011     |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 27.5       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 27          |\n",
            "|    time_elapsed         | 542         |\n",
            "|    total_timesteps      | 55296       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020846184 |\n",
            "|    clip_fraction        | 0.206       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.7       |\n",
            "|    explained_variance   | -0.00617    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.83        |\n",
            "|    n_updates            | 260         |\n",
            "|    policy_gradient_loss | -0.0211     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 23.7        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 4.02e+06   |\n",
            "|    total_cost           | 2.96e+05   |\n",
            "|    total_reward         | 3.02e+06   |\n",
            "|    total_reward_pct     | 302        |\n",
            "|    total_trades         | 77444      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 102        |\n",
            "|    iterations           | 28         |\n",
            "|    time_elapsed         | 562        |\n",
            "|    total_timesteps      | 57344      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03851261 |\n",
            "|    clip_fraction        | 0.291      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.7      |\n",
            "|    explained_variance   | 0.00668    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 11.3       |\n",
            "|    n_updates            | 270        |\n",
            "|    policy_gradient_loss | -0.0151    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 24.6       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.99e+06    |\n",
            "|    total_cost           | 2.82e+05    |\n",
            "|    total_reward         | 1.99e+06    |\n",
            "|    total_reward_pct     | 199         |\n",
            "|    total_trades         | 76557       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 29          |\n",
            "|    time_elapsed         | 582         |\n",
            "|    total_timesteps      | 59392       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024946528 |\n",
            "|    clip_fraction        | 0.257       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.7       |\n",
            "|    explained_variance   | 0.0694      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.68        |\n",
            "|    n_updates            | 280         |\n",
            "|    policy_gradient_loss | -0.0195     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 9.22        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.05e+06    |\n",
            "|    total_cost           | 2.89e+05    |\n",
            "|    total_reward         | 2.05e+06    |\n",
            "|    total_reward_pct     | 205         |\n",
            "|    total_trades         | 76425       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 30          |\n",
            "|    time_elapsed         | 602         |\n",
            "|    total_timesteps      | 61440       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.027853115 |\n",
            "|    clip_fraction        | 0.234       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.7       |\n",
            "|    explained_variance   | 0.0642      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 10.7        |\n",
            "|    n_updates            | 290         |\n",
            "|    policy_gradient_loss | -0.0153     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 14.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 31          |\n",
            "|    time_elapsed         | 622         |\n",
            "|    total_timesteps      | 63488       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.028772378 |\n",
            "|    clip_fraction        | 0.256       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.8       |\n",
            "|    explained_variance   | 0.0144      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 16.5        |\n",
            "|    n_updates            | 300         |\n",
            "|    policy_gradient_loss | -0.0219     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 39.1        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| environment/            |            |\n",
            "|    portfolio_value      | 2.91e+06   |\n",
            "|    total_cost           | 2.74e+05   |\n",
            "|    total_reward         | 1.91e+06   |\n",
            "|    total_reward_pct     | 191        |\n",
            "|    total_trades         | 75361      |\n",
            "| time/                   |            |\n",
            "|    fps                  | 102        |\n",
            "|    iterations           | 32         |\n",
            "|    time_elapsed         | 642        |\n",
            "|    total_timesteps      | 65536      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01723721 |\n",
            "|    clip_fraction        | 0.179      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.8      |\n",
            "|    explained_variance   | 0.0338     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 16.8       |\n",
            "|    n_updates            | 310        |\n",
            "|    policy_gradient_loss | -0.0137    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 27.6       |\n",
            "----------------------------------------\n",
            "day: 2767, episode: 35\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2911216.66\n",
            "total_reward: 1911216.66\n",
            "total_cost: 293661.84\n",
            "total_trades: 75716\n",
            "Sharpe: 0.714\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 2.91e+06    |\n",
            "|    total_cost           | 2.94e+05    |\n",
            "|    total_reward         | 1.91e+06    |\n",
            "|    total_reward_pct     | 191         |\n",
            "|    total_trades         | 75716       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 33          |\n",
            "|    time_elapsed         | 662         |\n",
            "|    total_timesteps      | 67584       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020937597 |\n",
            "|    clip_fraction        | 0.217       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.8       |\n",
            "|    explained_variance   | 0.00872     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 8.03        |\n",
            "|    n_updates            | 320         |\n",
            "|    policy_gradient_loss | -0.0205     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 14.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 4.52e+06    |\n",
            "|    total_cost           | 2.96e+05    |\n",
            "|    total_reward         | 3.52e+06    |\n",
            "|    total_reward_pct     | 352         |\n",
            "|    total_trades         | 76771       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 34          |\n",
            "|    time_elapsed         | 682         |\n",
            "|    total_timesteps      | 69632       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025822021 |\n",
            "|    clip_fraction        | 0.248       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.8       |\n",
            "|    explained_variance   | -0.0164     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 10.7        |\n",
            "|    n_updates            | 330         |\n",
            "|    policy_gradient_loss | -0.0163     |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 17.4        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 102        |\n",
            "|    iterations           | 35         |\n",
            "|    time_elapsed         | 702        |\n",
            "|    total_timesteps      | 71680      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03445168 |\n",
            "|    clip_fraction        | 0.338      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.8      |\n",
            "|    explained_variance   | 0.0655     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 16.4       |\n",
            "|    n_updates            | 340        |\n",
            "|    policy_gradient_loss | -0.0176    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 29.6       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.85e+06    |\n",
            "|    total_cost           | 2.88e+05    |\n",
            "|    total_reward         | 2.85e+06    |\n",
            "|    total_reward_pct     | 285         |\n",
            "|    total_trades         | 77050       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 36          |\n",
            "|    time_elapsed         | 722         |\n",
            "|    total_timesteps      | 73728       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.028852897 |\n",
            "|    clip_fraction        | 0.218       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.9       |\n",
            "|    explained_variance   | -0.0382     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.16        |\n",
            "|    n_updates            | 350         |\n",
            "|    policy_gradient_loss | -0.00928    |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 14.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.58e+06    |\n",
            "|    total_cost           | 2.83e+05    |\n",
            "|    total_reward         | 2.58e+06    |\n",
            "|    total_reward_pct     | 258         |\n",
            "|    total_trades         | 75771       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 37          |\n",
            "|    time_elapsed         | 742         |\n",
            "|    total_timesteps      | 75776       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.044501018 |\n",
            "|    clip_fraction        | 0.293       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.9       |\n",
            "|    explained_variance   | 0.0476      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.72        |\n",
            "|    n_updates            | 360         |\n",
            "|    policy_gradient_loss | -0.00468    |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 15.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 4.97e+06    |\n",
            "|    total_cost           | 2.99e+05    |\n",
            "|    total_reward         | 3.97e+06    |\n",
            "|    total_reward_pct     | 397         |\n",
            "|    total_trades         | 76614       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 38          |\n",
            "|    time_elapsed         | 763         |\n",
            "|    total_timesteps      | 77824       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.039605655 |\n",
            "|    clip_fraction        | 0.294       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.9       |\n",
            "|    explained_variance   | -0.00347    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.88        |\n",
            "|    n_updates            | 370         |\n",
            "|    policy_gradient_loss | -0.0176     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 17.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 39          |\n",
            "|    time_elapsed         | 783         |\n",
            "|    total_timesteps      | 79872       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.029243696 |\n",
            "|    clip_fraction        | 0.23        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44         |\n",
            "|    explained_variance   | 0.0331      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 22.6        |\n",
            "|    n_updates            | 380         |\n",
            "|    policy_gradient_loss | -0.015      |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 47.4        |\n",
            "-----------------------------------------\n",
            "day: 2767, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3313090.92\n",
            "total_reward: 2313090.92\n",
            "total_cost: 279467.75\n",
            "total_trades: 76035\n",
            "Sharpe: 0.819\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.31e+06    |\n",
            "|    total_cost           | 2.79e+05    |\n",
            "|    total_reward         | 2.31e+06    |\n",
            "|    total_reward_pct     | 231         |\n",
            "|    total_trades         | 76035       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 40          |\n",
            "|    time_elapsed         | 804         |\n",
            "|    total_timesteps      | 81920       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025377465 |\n",
            "|    clip_fraction        | 0.244       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44         |\n",
            "|    explained_variance   | -0.0872     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.49        |\n",
            "|    n_updates            | 390         |\n",
            "|    policy_gradient_loss | -0.0124     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 13.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.34e+06    |\n",
            "|    total_cost           | 2.83e+05    |\n",
            "|    total_reward         | 2.34e+06    |\n",
            "|    total_reward_pct     | 234         |\n",
            "|    total_trades         | 75169       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 41          |\n",
            "|    time_elapsed         | 824         |\n",
            "|    total_timesteps      | 83968       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.050513558 |\n",
            "|    clip_fraction        | 0.343       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44         |\n",
            "|    explained_variance   | 0.0155      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.8         |\n",
            "|    n_updates            | 400         |\n",
            "|    policy_gradient_loss | -0.0125     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 13          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 4.2e+06     |\n",
            "|    total_cost           | 2.8e+05     |\n",
            "|    total_reward         | 3.2e+06     |\n",
            "|    total_reward_pct     | 320         |\n",
            "|    total_trades         | 75265       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 42          |\n",
            "|    time_elapsed         | 844         |\n",
            "|    total_timesteps      | 86016       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.070156105 |\n",
            "|    clip_fraction        | 0.377       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.1       |\n",
            "|    explained_variance   | 0.0712      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 9.26        |\n",
            "|    n_updates            | 410         |\n",
            "|    policy_gradient_loss | -0.0114     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 18.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 43          |\n",
            "|    time_elapsed         | 864         |\n",
            "|    total_timesteps      | 88064       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.037101034 |\n",
            "|    clip_fraction        | 0.291       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.2       |\n",
            "|    explained_variance   | 0.0168      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 19.8        |\n",
            "|    n_updates            | 420         |\n",
            "|    policy_gradient_loss | -0.00773    |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 46.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.95e+06    |\n",
            "|    total_cost           | 2.93e+05    |\n",
            "|    total_reward         | 2.95e+06    |\n",
            "|    total_reward_pct     | 295         |\n",
            "|    total_trades         | 76013       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 44          |\n",
            "|    time_elapsed         | 884         |\n",
            "|    total_timesteps      | 90112       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.036360547 |\n",
            "|    clip_fraction        | 0.371       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.2       |\n",
            "|    explained_variance   | 0.0583      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.99        |\n",
            "|    n_updates            | 430         |\n",
            "|    policy_gradient_loss | -0.0113     |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 11.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 5.2e+06     |\n",
            "|    total_cost           | 2.91e+05    |\n",
            "|    total_reward         | 4.2e+06     |\n",
            "|    total_reward_pct     | 420         |\n",
            "|    total_trades         | 75716       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 45          |\n",
            "|    time_elapsed         | 904         |\n",
            "|    total_timesteps      | 92160       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.053087823 |\n",
            "|    clip_fraction        | 0.382       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.3       |\n",
            "|    explained_variance   | 0.0321      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 10          |\n",
            "|    n_updates            | 440         |\n",
            "|    policy_gradient_loss | -0.0118     |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 23.7        |\n",
            "-----------------------------------------\n",
            "day: 2767, episode: 45\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4246753.37\n",
            "total_reward: 3246753.37\n",
            "total_cost: 285796.77\n",
            "total_trades: 75278\n",
            "Sharpe: 0.931\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 4.25e+06    |\n",
            "|    total_cost           | 2.86e+05    |\n",
            "|    total_reward         | 3.25e+06    |\n",
            "|    total_reward_pct     | 325         |\n",
            "|    total_trades         | 75278       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 46          |\n",
            "|    time_elapsed         | 924         |\n",
            "|    total_timesteps      | 94208       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.041236922 |\n",
            "|    clip_fraction        | 0.294       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.4       |\n",
            "|    explained_variance   | 0.000647    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 58.9        |\n",
            "|    n_updates            | 450         |\n",
            "|    policy_gradient_loss | -0.00646    |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 106         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 47          |\n",
            "|    time_elapsed         | 944         |\n",
            "|    total_timesteps      | 96256       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.039148763 |\n",
            "|    clip_fraction        | 0.295       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.4       |\n",
            "|    explained_variance   | 0.0497      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 19.9        |\n",
            "|    n_updates            | 460         |\n",
            "|    policy_gradient_loss | -0.00989    |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 43.5        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 4.03e+06    |\n",
            "|    total_cost           | 2.89e+05    |\n",
            "|    total_reward         | 3.03e+06    |\n",
            "|    total_reward_pct     | 303         |\n",
            "|    total_trades         | 75583       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 48          |\n",
            "|    time_elapsed         | 965         |\n",
            "|    total_timesteps      | 98304       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024677718 |\n",
            "|    clip_fraction        | 0.317       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.4       |\n",
            "|    explained_variance   | 0.106       |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.71        |\n",
            "|    n_updates            | 470         |\n",
            "|    policy_gradient_loss | -0.0123     |\n",
            "|    std                  | 1.06        |\n",
            "|    value_loss           | 9.92        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| environment/            |             |\n",
            "|    portfolio_value      | 3.28e+06    |\n",
            "|    total_cost           | 2.92e+05    |\n",
            "|    total_reward         | 2.28e+06    |\n",
            "|    total_reward_pct     | 228         |\n",
            "|    total_trades         | 75623       |\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 49          |\n",
            "|    time_elapsed         | 985         |\n",
            "|    total_timesteps      | 100352      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024389988 |\n",
            "|    clip_fraction        | 0.167       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.4       |\n",
            "|    explained_variance   | 0.0901      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 14.4        |\n",
            "|    n_updates            | 480         |\n",
            "|    policy_gradient_loss | -0.0128     |\n",
            "|    std                  | 1.07        |\n",
            "|    value_loss           | 23.4        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2020-01-02 to  2020-04-02\n",
            "PPO Sharpe Ratio:  -0.453022435009067\n",
            "======DDPG Training========\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ddpg_378_1\n",
            "day: 2767, episode: 50\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3230151.80\n",
            "total_reward: 2230151.80\n",
            "total_cost: 1616.05\n",
            "total_trades: 36758\n",
            "Sharpe: 0.763\n",
            "=================================\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 3.09e+06 |\n",
            "|    total_cost       | 1.69e+03 |\n",
            "|    total_reward     | 2.09e+06 |\n",
            "|    total_reward_pct | 209      |\n",
            "|    total_trades     | 41182    |\n",
            "| time/               |          |\n",
            "|    episodes         | 4        |\n",
            "|    fps              | 34       |\n",
            "|    time_elapsed     | 320      |\n",
            "|    total timesteps  | 11072    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | -8.02    |\n",
            "|    critic_loss      | 11.2     |\n",
            "|    learning_rate    | 5e-06    |\n",
            "|    n_updates        | 8304     |\n",
            "----------------------------------\n",
            "======DDPG Validation from:  2020-01-02 to  2020-04-02\n",
            "======Best Model Retraining from:  2009-01-01 to  2020-04-02\n",
            "{'action_noise': OrnsteinUhlenbeckActionNoise(mu=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0.], sigma=[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
            " 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]), 'buffer_size': 50000, 'learning_rate': 5e-06, 'batch_size': 128}\n",
            "Using cpu device\n",
            "Logging to tensorboard_log/ddpg/ensemble_378_1\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 3.49e+06 |\n",
            "|    total_cost       | 1.26e+03 |\n",
            "|    total_reward     | 2.49e+06 |\n",
            "|    total_reward_pct | 249      |\n",
            "|    total_trades     | 36231    |\n",
            "| time/               |          |\n",
            "|    episodes         | 4        |\n",
            "|    fps              | 34       |\n",
            "|    time_elapsed     | 325      |\n",
            "|    total timesteps  | 11324    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 29.4     |\n",
            "|    critic_loss      | 28.3     |\n",
            "|    learning_rate    | 5e-06    |\n",
            "|    n_updates        | 8493     |\n",
            "----------------------------------\n",
            "======Trading from:  2020-04-02 to  2020-07-02\n",
            "Ensemble Strategy took:  162.4008615533511  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0qd8acMtj1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "34e407ec-0b6e-42fb-e707-0c577a79dc74"
      },
      "source": [
        "df_summary"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-78e5cde76b38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_summary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df_summary' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4JKB--8tj1g"
      },
      "source": [
        "unique_trade_date = processed_full[(processed_full.date > val_test_start)&(processed_full.date <= val_test_end)].date.unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "q9mKF7GGtj1g",
        "outputId": "5de4e5e6-d74b-4d12-b786-7e299dabd6a5"
      },
      "source": [
        "df_trade_date = pd.DataFrame({'datadate':unique_trade_date})\n",
        "\n",
        "df_account_value=pd.DataFrame()\n",
        "for i in range(rebalance_window+validation_window, len(unique_trade_date)+1,rebalance_window):\n",
        "    temp = pd.read_csv('results/account_value_trade_{}_{}.csv'.format('ensemble',i))\n",
        "    df_account_value = df_account_value.append(temp,ignore_index=True)\n",
        "sharpe=(252**0.5)*df_account_value.account_value.pct_change(1).mean()/df_account_value.account_value.pct_change(1).std()\n",
        "print('Sharpe Ratio: ',sharpe)\n",
        "df_account_value=df_account_value.join(df_trade_date[validation_window:].reset_index(drop=True))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sharpe Ratio:  0.17377457928215234\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyosyW7_tj1g",
        "outputId": "29307dfb-1d27-4fb7-dc7a-7a34a4902d59"
      },
      "source": [
        "df_account_value.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account_value</th>\n",
              "      <th>date</th>\n",
              "      <th>daily_return</th>\n",
              "      <th>datadate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>500000.000000</td>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>498557.292308</td>\n",
              "      <td>2019-04-02</td>\n",
              "      <td>-0.002885</td>\n",
              "      <td>2019-04-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>497280.859251</td>\n",
              "      <td>2019-04-03</td>\n",
              "      <td>-0.002560</td>\n",
              "      <td>2019-04-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>515844.876585</td>\n",
              "      <td>2019-04-04</td>\n",
              "      <td>0.037331</td>\n",
              "      <td>2019-04-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>496792.628660</td>\n",
              "      <td>2019-04-05</td>\n",
              "      <td>-0.036934</td>\n",
              "      <td>2019-04-05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   account_value        date  daily_return    datadate\n",
              "0  500000.000000  2019-04-01           NaN  2019-04-01\n",
              "1  498557.292308  2019-04-02     -0.002885  2019-04-02\n",
              "2  497280.859251  2019-04-03     -0.002560  2019-04-03\n",
              "3  515844.876585  2019-04-04      0.037331  2019-04-04\n",
              "4  496792.628660  2019-04-05     -0.036934  2019-04-05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLsRdw2Ctj1h",
        "outputId": "01dd70f8-639f-49f7-8e75-60ebc88b0e4e"
      },
      "source": [
        "%matplotlib inline\n",
        "df_account_value.account_value.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "1053083a-d74c-48b0-a623-de33282e2fff"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = BackTestStats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return         -0.021889\n",
            "Cumulative returns    -0.037990\n",
            "Annual volatility      0.459591\n",
            "Sharpe ratio           0.173775\n",
            "Calmar ratio          -0.034518\n",
            "Stability              0.327646\n",
            "Max drawdown          -0.634133\n",
            "Omega ratio            1.033623\n",
            "Sortino ratio          0.276953\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.986957\n",
            "Daily value at risk   -0.057586\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "scrolled": false,
        "outputId": "4f77cef2-3934-444a-cacc-4ed8f94514ae"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "BackTestPlot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = df_account_value.loc[0,'date'],\n",
        "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to IHSG===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (430, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-04-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-01-07</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>20</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>-3.076%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>-5.18%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>46.128%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>-0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-63.413%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>2.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>19.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-5.783%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>-0.17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.41</td>\n",
              "      <td>2019-04-26</td>\n",
              "      <td>2020-04-07</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.27</td>\n",
              "      <td>2019-04-05</td>\n",
              "      <td>2019-04-15</td>\n",
              "      <td>2019-04-23</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.49</td>\n",
              "      <td>2019-04-24</td>\n",
              "      <td>2019-04-25</td>\n",
              "      <td>2019-04-26</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.26</td>\n",
              "      <td>2019-04-02</td>\n",
              "      <td>2019-04-04</td>\n",
              "      <td>2019-04-05</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlLT9_5WN478"
      },
      "source": [
        "<a id='6.3'></a>\n",
        "## 7.3 Baseline Stats"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YktexHcqh1jc",
        "outputId": "38566531-a3a0-4705-db30-d437e8f8fc73"
      },
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_perf_stats=BaselineStats('^DJI',\n",
        "                                  baseline_start = df_account_value.loc[0,'date'],\n",
        "                                  baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (430, 8)\n",
            "Annual return         -0.027420\n",
            "Cumulative returns    -0.046334\n",
            "Annual volatility      0.216841\n",
            "Sharpe ratio          -0.020512\n",
            "Calmar ratio          -0.069439\n",
            "Stability              0.398216\n",
            "Max drawdown          -0.394883\n",
            "Omega ratio            0.996064\n",
            "Sortino ratio         -0.028793\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.041591\n",
            "Daily value at risk   -0.027337\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}